In an ordinary voice recognition system, Fast Fourier Transformation (FFT) is used on an analog/digital-converted input signal for the purpose of spectral analysis of the input signal. One considerable problem within the context of voice recognition, generally within the context of pattern recognition, is the suppression of interference noise, expressed another way the suppression of noise signals. The interference noise causes the recognition rate to fall drastically even at relatively low levels of existing interference signals.
To suppress interference noise, it is known practice in A. Adami et al., Qualcomm-ICSI-OGI Features for ASR, ICSLP-2002, Denver, Colo., USA, September 2002 to use a Wiener filter as an adaptive filter in order to increase the signal-to-noise ratio during signal analysis.
A drawback of the use of a Wiener filter, generally of an adaptive filter, within the context of interference noise suppression can be seen, in particular, in the very great computation complexity for forming the filter algorithm and in the need for periodically repeated calculation of the filter coefficients.
H. Hermansky et al., RASTA-PLP Speech Analysis, International Computer Science Institute Technical Report (ICSI TR) 91-069, Berkeley, Calif., December 1991 also describes a method for voice recognition in which an analog/digital-converted signal is used to form intermediate feature vectors which are subjected to bandpass filtering.
The method described in H. Hermansky et al., RASTA-PLP Speech Analysis, International Computer Science Institute Technical Report (ICSI TR) 91-069, Berkeley, Calif., December 1991 has, in particular, the drawback of a still relatively poor recognition power within the context of voice recognition for a voice signal which is affected by an interference signal.
In addition, H.-G. Hirsch and D. Pearce, The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions, ISCA IPRW ASR 2000, Automatic speech recognition: Challenges for the next millennium, Paris, France, 18-20 Sep. 2000 describes the “AURORA” experimental framework for ascertaining the performance of a voice recognition system in an environment with interference signals.
DE 35 10 660 C2 describes a method and a device for processing a voice signal in which the voice signal is subjected to frequency analysis, the frequency distribution pattern (obtained in the result of the frequency analysis on the voice signal) in a particular frequency range over which the voice signal extends being repeated alternately along a time axis in order to form a periodic waveform which is subjected to high-pass filtering, which extracts the relatively quickly changing components.
In addition, DE 41 11 995 A1 discloses the practice of logarithmizing feature vectors during Fast-Fourier-Transformation-based spectral analysis prior to the convolution. DE 41 11 995 A1 also discloses the practice of performing intensity normalization of the spectral feature vectors prior to recursive high-pass filtering which is applied to the voice signal.
A. Adami et al., “Qualcomm-ICSI-OGI features for ASR,” in Proc. International Conference on Spoken Language Processing (ICSLP '02). An archive with an additional description of the Wiener filter with associated software can be obtained at the following URL address: http://www.icsi.berkeley.edu/Speech/papers/gelbart-ms/pointers discloses a Wiener filter.
There is a need for providing pattern recognition which has an improved recognition rate over the method described in H. Hermansky et al., RASTA-PLP Speech Analysis, International Computer Science Institute Technical Report (ICSI TR) 91-069, Berkeley, Calif., December 1991 and requires less computation complexity than the method described in A. Adami et al., Qualcomm-ICSI-OGI Features for ASR, ICSLP-2002, Denver, Colo., USA, September 2002.